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Ultrafast Laser TTM Toolbox (Python)

Python solvers for ultrafast pulsed-laser heating of metals, built on the two-temperature model (TTM). The package spans single-pulse electron-lattice dynamics on femtosecond timescales, heat accumulation over thousands of pulses, and moving-beam scans.

This is a port of the MATLAB Ultrafast Laser TTM Toolbox, validated solver by solver against that reference. Both implement the model published in:

Fieser, D., Dewanjee, U. N., and Hu, A. (2026). A Computationally Efficient Two-Stage Two-Temperature Model for Multi-pulse Femtosecond Laser Heat Accumulation in Tungsten. Journal of Materials Engineering and Performance. doi:10.1007/s11665-026-14738-6

Where to go

Page	What it covers
Getting Started	Install, first run, what output to expect, editing parameters
Solver Reference	Every solver with its config fields, defaults, units, and returned results
Model Background	The TTM equations, the two-stage strategy, the surface inversion, material presets
Command Line and MCP	The laserttm command, and driving the solvers from an AI assistant
Results and Outputs	The shared result contract, output files, and the outputs folder
Validation	How agreement with the MATLAB reference is established and measured
FAQ	Common questions and troubleshooting
Citing	How to cite the paper and the software

Quick facts

• Install: pip install laserttm. Python 3.10 or newer.
• Dependencies: NumPy, SciPy, Numba, Matplotlib. The MCP server is an optional extra.
• Solvers: 0D surface point, 1D depth-resolved, radial profile, single-pulse visualizer, inversion quantifier, scanning beam.
• Speed: the 50-pulse baseline runs in about 0.07 s once the kernels are compiled, roughly 100 times faster than the MATLAB reference on the same machine. See Validation for the full table.
• Materials: presets for tungsten, copper, gold, and aluminum, plus a custom mode for any metal.
• Interfaces: Python API, a laserttm command-line tool, and an MCP server for AI assistants.
• Self-describing: every config key and every results key, with units and array axes, comes from laserttm describe; validate shows the fluence and pulse count a config implies before it runs.
• License: MIT.
• Software archive: Zenodo concept DOI 10.5281/zenodo.22210435.

Thirty-second tour

from laserttm import surface_point_solver

cfg = {
    "material": "W",          # tungsten preset
    "Pavg": 10,               # average power [W]
    "spotRadius": 100e-6,     # 1/e^2 spot radius [m]
    "f_rep": 5e6,             # repetition rate [Hz]
    "tau_FWHM": 500e-15,      # pulse width, FWHM [s]
}
cfg["simDuration"] = 50 / cfg["f_rep"]   # 50 pulses

results = surface_point_solver(cfg)
print(results["finalResid_C"])

This solves 50 pulses of coupled electron-lattice dynamics with inter-pulse thermal diffusion. The run prints a summary, writes a text report under outputs/, and returns the full surface temperature history. Continue with Getting Started.

Finding the fields without reading source

Every solver describes itself, so config keys never have to be guessed:

from laserttm import describe_solver, validate_config

describe_solver("depth_profile")        # every key with unit, default, range
validate_config("depth_profile", cfg)   # check a config in milliseconds

The same information is available from the command line as laserttm describe depth_profile. See Command Line and MCP.